public void EvaluateGeneration()
{
_lastGenerationResults.Clear();
_evaluatedGenomes.Clear();
for (int i = 0; i < _genomes.Count; i++)
{
var genome = _genomes[i];
var fitnessGenome = new FitnessGenome()
{
Fitness = EvaluateGenome(genome), Genome = genome
};
_evaluatedGenomes.Add(fitnessGenome);
}
//Sort Collections
_evaluatedGenomes = _evaluatedGenomes.OrderByDescending(g => g.Fitness).ToList();
_lastGenerationResults.AddRange(_evaluatedGenomes);
//kill the 9/10 worst genomes
var cutoffIndex = _evaluatedGenomes.Count / 10;
_evaluatedGenomes = _evaluatedGenomes.Take(cutoffIndex).ToList();
//find next generation population
_nextGeneration.Clear();
// save champ
var champion = _evaluatedGenomes.First();
FittestGenome = champion;
_nextGeneration.Add(champion.Genome);
while (_nextGeneration.Count() < _neatConfiguration.PopulationSize)
{
if (_random.NextDouble() > _neatConfiguration.ASexualReproductionRate)
{
var parent1 = _evaluatedGenomes[_random.Next(_evaluatedGenomes.Count)];
FitnessGenome parent2;
do
{
parent2 = _evaluatedGenomes[_random.Next(_evaluatedGenomes.Count)];
} while (parent2 == parent1);
Genome child;
if (parent1.Fitness > parent2.Fitness)
{
child = _crossFunctionStrategy.GenerateOffspring(parent1.Genome, parent2.Genome);
}
else
{
child = _crossFunctionStrategy.GenerateOffspring(parent2.Genome, parent1.Genome);
}
if (_random.NextDouble() < _neatConfiguration.MutationRate)
{
_weightMutation.Mutate(ref child);
}
if (_random.NextDouble() < _neatConfiguration.AddConnectionRate)
{
_addConnectionMutation.Mutate(ref child);
}
if (_random.NextDouble() < _neatConfiguration.AddNodeRate)
{
_addNodeMutation.Mutate(ref child);
}
_nextGeneration.Add(child);
}
else
{
var parent = _evaluatedGenomes[_random.Next(_evaluatedGenomes.Count)];
var child = parent.Genome.Copy();
_weightMutation.Mutate(ref child);
_nextGeneration.Add(child);
}
}
//Transfer next generation to current generation
_genomes.Clear();
_genomes.AddRange(_nextGeneration);
}
